1. Field
The disclosure relates generally to a computer implemented method, a computer program product, and a data processing system. More specifically, the disclosure relates to a computer implemented method, a computer program product, and a data processing system for managing an execution mode for a simultaneous multi-threaded processor.
2. Description of the Related Art
Increasingly large symmetric multi-processor data processing systems are not being used as single large data processing systems. Instead, these types of data processing systems are being partitioned and used as smaller systems. These systems are also referred to as logical partitioned (LPAR) data processing systems. A logical partitioned functionality within a data processing system allows multiple copies of a single operating system or multiple heterogeneous operating systems to be simultaneously run on a single data processing system platform. A partition, within which an operating system image runs, is assigned a non-overlapping subset of the platform's resources. These platform allocable resources include one or more architecturally distinct processors and their interrupt management area, regions of system memory, and input/output (I/O) adapter bus slots. The partition's resources are represented by the platform's firmware to the operating system image.
Each distinct operating system or image of an operating system running within a platform is protected from each other, such that software errors on one logical partition cannot affect the correct operation of any of the other partitions. This protection is provided by allocating a disjointed set of platform resources to be directly managed by each operating system image and by providing mechanisms for ensuring that the various images cannot control any resources that have not been allocated to that image. Furthermore, software errors in the control of an operating system's allocated resources are prevented from affecting the resources of any other image. Thus, each image of the operating system, or each different operating system, directly controls a distinct set of allocable resources within the platform.
With respect to hardware resources in a logical partitioned data processing system, these resources are shared dis-jointly among various partitions. These resources may include, for example, input/output (I/O) adapters, memory DIMMs, non-volatile random access memory (NVRAM), and hard disk drives. Each partition within a logical partitioned data processing system may be booted and shut down over and over without having to power-cycle the entire data processing system.
Parallel processing is a form of computation in which many calculations are carried out simultaneously. Large problems and operations are divided into smaller pieces. These smaller pieces are then solved concurrently, or “in parallel”. Parallel processing can be implemented at several different computing levels, including the bit-level, the instruction level, the data level, and task parallelism.
Large problems executing in parallel could theoretically be divided into any number of parallel parts. However, at a certain level of parallelism, the benefits of parallel processing diminish. Beyond a certain level of parallelism, instructions may even require more clock cycles to complete than at a lower level of parallelism. This negative scaling of additional parallel parts is known as parallel slowdown.
Parallel slowdown is typically the result of a communications bottleneck. As more processing nodes are added, that is, as the level of parallelism increases, each processing node spends progressively more time doing communication than useful processing. Beyond a certain level of parallelism, the communications overhead created by adding additional processing nodes, surpasses the increased processing power that those nodes provide. When the loss from communications overhead becomes greater than the increased processing power from additional nodes, parallel slowdown occurs.